Hybrid Machine Learning Forest Height Estimation from TanDEM-X InSAR

Abstract

Combining machine learning with physical models can significantly impact retrieval algorithms designed to invert geophysical parameters from remote sensing data. Such hybrid models integrate physical knowledge with domain expertise through a joint architecture, potentially enhancing performance by increasing the efficiency and flexibility of the physical model as well as the generalization and interpretability of the machine learning predictions. This work introduces a hybrid model for estimating forest height using single-baseline, single-polarization TanDEM-X interferometric coherence measurements. In this model, the vertical reflectivity profile is derived as a function of input features, including topographic and acquisition geometry descriptors, using a multilayer perceptron network. This profile is then used to invert forest height by leveraging the established physical relationship connecting the vertical reflectivity profile to forest height. The developed model is applied and validated on several TanDEM-X acquisitions over tropical sites with different acquisition geometries, and its performance is assessed against reference data derived from airborne LiDAR measurements.